The opioid epidemic in the US is a public health emergency, exacerbated by the Covid-19 pandemic. Medi- cations for opioid use disorder (MOUD)-injection naltrexone, buprenorphine, and methadone-are the most effective tools for improving outcomes and preventing overdose among persons with OUD, but engagement in MOUD, especially long-term engagement typically required for a successful outcome, is unacceptably low. Long-term engagement rates tend to be even lower in real-world settings-what NIDA has termed the research-to-practice gap. This discrepancy between trial and real-world MOUD effectiveness could be par- tially attributable to differences between clinical trial versus real-world population characteristics (e.g., in terms of psychiatric and substance use comorbidities, previous treatment experience, immigration status, etc.) if treatment effects are modified (increased/decreased) by some of these characteristics that also relate to trial participation. Moreover, without knowing the relative effectiveness of MOUDs for certain real-world target pop- ulations, clinicians, researchers, and policymakers may be tasked with decision-making with biased evidence. Thus, there is a critical need to improve the generalizability of MOUD trials. Failing to meet this need would further ossify the research-to-practice gap, resulting in suboptimal treatment of OUD overall and within key subgroups. We propose to develop design and analytic approaches, what we call a generalizability through- line, to bridge MOUD trial evidence to real-world populations. The objectives of this project are: In Aim 1), to identify and characterize clinically meaningful, interpretable subgroups of persons seeking OUD treatment in US usual-care settings who are not represented or under-represented in MOUD trials based on multiple char- acteristics simultaneously. This will move us beyond existing approaches for assessing representation that have generally been limited to considering one individual-level characteristic at a time (e.g., race/ethnicity). We will apply the approach developed in the first part of Aim 1 to trial data (3 MOUD trials from NIDA CTN) and population data (California and New Jersey Medicaid claims) to characterize under-represented subgroups. In Aim 2), to generalize MOUD effectiveness to state-specific adult Medicaid populations, thereby estimating a realistic treatment goal if treatment retention supports, incentives, and dosing practices were improved to align with those in trials. Existing approaches for predicting generalized effects rely on extrapolation for non- and under-represented subgroups, which can result in biased and/or uninformative estimates. The approach developed in the first part of Aim 2 will make several improvements to limit extrapolation and increase effi- ciency. In Aim 3), to implement the methods developed for Aims 1 and 2 in user-friendly software to facilitate the easy adoption by applied trialists, researchers, and clinicians. The proposed research is expected to make a significant contribution to improving representation among trial participants and to understanding how and to whom trial findings generalize.

Medication treatments for opioid use disorder (MOUD) are the most effective tools for improving outcomes and preventing overdose among persons with OUD. However, there is a significant discrepancy between MOUD effectiveness achieved in clinical trials and in real world settings-what NIDA has termed the research-to- practice gap-which may be partially attributable to differences between clinical trial populations and those in the real world. In this work, we will unite design and analytic strategies to bridge MOUD trial evidence to target populations by: 1) empirically identifying those individuals not represented or under-represented in MOUD trials, and 2) accurately and reliably generalizing expected MOUD effects on treatment retention to the adult Medicaid population, with the broad goal of optimizing the generalizability and policy-relevance of future clinical trials.